This research from Penn State and KiNiT, benchmarks the effectiveness of 10 authorship obfuscation (AO) techniques against 37 machine-generated text (MGT) detection methods across 11 languages, totaling 4,070 evaluations. It reveals that all AO methods can evade detection in every language, particularly highlighting the efficacy of homoglyph attacks. This underscores the need for improved multilingual MGT detection strategies.

This research project is a collaboration with Penn State and MIT Lincoln Lab. Our study demonstrates the dual capacity of LLMs for offensive misuse and defense detection against disinformation without requiring additional training.

This research from Penn State and KiNiT introduces MULTITuDE, a novel multilingual dataset for detecting machine-generated text. Comprised of over 74,000 authentic and artificially-generated texts in 11 languages from 8 models, MULTITuDE benchmarks text generation capabilities in non-English languages and multilingual detection performance. The dataset addresses current gaps in analyzing and systematically evaluating machine text generation and detection across multiple languages.

This paper is the first attempt to detect COVID-19 misinformation (in English, Spanish, and Haitian French) populated in the Caribbean regions, using the fact-checked claims in the US (in English).